Unleash your business potential with self-service analytics

Many businesses look for new ways to unleash their data’s potential. They struggle to manage data effectively and enable business insights and innovation that create value for the organisation.

These organisations seek new ways to make their data more accessible to business users and avoid the traditional bottlenecks of centralised data and analytics development. In many organisations, data and analytics services are provided by centralised departments that often struggle to balance short-term business priorities and long-term efforts to build a strong, unified data and analytics foundation. 

For this reason, many organisations embark on a self-service journey to improve both agility and time to market for data and insights delivery, allowing line-of-business employees to access data that enable the organisation as a whole to make better decisions and value-creating innovations. 

Deloitte strongly encourages clients to embark on the self-service journey and take advantage of these benefits, while steering clear of the common pitfalls associated with self-service analytics.

Avoid the common pitfalls

The most common pitfall we observe is organisations seeing self-service analytics as a shortcut that allows them to bypass the need for a strong data foundation, failing to recognise underlying challenges such as lacking organisational capabilities and maturity to manage, govern and leverage data effectively.  

However, understanding both your organisation’s capabilities and maturity and the potential and limitations of self-service analytics is essential. Simply providing regular business users with access to non-curated, ‘raw’ data will only lead to conflicting numbers and incorrect insights. Without the right people and the ability to manage and leverage data, your organisation will not be able to build and sustain a self-service analytics capability that enables business users to create insights on their own. 

In this context, there are two types of organisations, and they part from different starting points on the journey of self-service:

• The data-savvy organisation uses well-structured data and is characterised by strong organisational competencies and data literacy. The aspiration to enable self-service analytics is a sign of this organisation’s maturity and driven by clear business drivers and real value potential.
• The less data-savvy organisation considers self-service analytics a shortcut to mitigate existing troubles and bottlenecks caused by centralised analytics delivery. To this organisation, lack of organisational capabilities and maturity represent an underlying challenge.

Identifying which type of organisation yours resembles the most and understanding how self-service analytics can be leveraged to create value will help you understand how and when self-service analytics can be most helpful in your organisation and how your organisation can become more data-savvy.

So, let’s take a closer look at what self-service analytics means and how it can be leveraged by different types of data consumers and specialists to create business value.

Self-service analytics done right: democratising and decentralising data

On an organisational level, self-service analytics democratises and decentralises data by making them widely accessible. As discussed earlier, this reduces bottlenecks that derive from a centralised data set-up, helping speed up analytics delivery and achieve greater agility and flexibility.

On a practical level, self-service analytics covers a variety of scenarios, from traditional self-service aimed at regular business users who consume data and do not possess any special skills to data science labs aimed at highly data-savvy super users and data scientists who can organise unexplored data, build sophisticated reports and organise data in a sandbox environment.

Traditionally, self-service analytics offers organisations a simplified model of the organisation’s data and simple-to-use BI tools, enabling line-of-business professionals to explore data opportunities, perform analyses and build reports that match their specific needs, ultimately empowering them to use data to drive innovation and business development.

This creates value by allowing business users to complete low-end analytical tasks, relieving more highly skilled analytics professionals who can then focus on curating data, building a data foundation and enabling more people to contribute to creating more insights, innovations and value from the same data.

The highly skilled super users and data scientist create value through their ability to explore data from different sources and to create more advanced insights and applications in the data science laboratory. With fewer requirements in terms of data preparation, a data science lab enables business innovation that is potentially disruptive but limited to people with specialist skills and therefore also limited in terms of organisational scale.

The path to self-service success

To enable self-service analytics that truly enables regular business users to leverage data effectively, organisations need to invest in sourcing and consolidating data to build a strong, curated data foundation. Traditionally, this involves significant, long-term investment and specialised resources.

However, by recognising how different types of users complement each other, organisations can define a more flexible and incremental path to build and mature their self-service analytics capability, capturing business value incrementally as well: Employing a few, highly skilled data explorers and data scientists to explore and organise data within specific business domains will help mature and curate data, paving the way for regular business users, which in turn enables the organisation to significantly increase insights and innovation development and capture business value.

As organisations roll out self-service analytics, data literacy and a data-driven culture are cultivated, which in the long term empowers business users to navigate more advanced data models and link data from various sources. The result is a well-rooted, sustainable organisational data transformation.

Future developments

For the time being, non-curated ‘raw data for the masses’ is not a viable path to pursue, but it may be in the future. New technology consistently aims to assist regular business users in their work and help them navigate and build new data models, expanding the use cases for self-service analytics for regular business users. New cloud-based technology, including low-code and no-code tools, also introduces opportunities for new business user groups to use their tools of choice and source data from third parties. In turn, these future developments will lower the bar in terms of how prepared data need to be and will further empower business users to navigate data models and link data from different sources.

In our next article …

… We take a closer look at the self-service journey and how your organisation can successfully implement and manage self-service analytics. We will also dive into how your organisation can build organisational capabilities and develop a mindset that enables self-service success.

Do you want to know how self-service analytics can help your business? Reach out to our experts.